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Register a new userPredicting Intraoperative Hypoxemia with Joint Sequence Autoencoder Networks
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Hanyang Liu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We present an end-to-end model using streaming physiological time series to accurately predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Our proposed model makes inference on both hypoxemia outcomes and future input sequences, enabled by a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learns future-indicative latent representation. All decoders share a memory-based encoder that helps capture the global dynamics of patient data. In a large surgical cohort of 73,536 surgeries at a major academic medical center, our model outperforms all baselines and gives a large performance gain over the state-of-the-art hypoxemia prediction system. With a high sensitivity cutoff at 80%, it presents 99.36% precision in predicting hypoxemia and 86.81% precision in predicting the much more severe and rare hypoxemic condition, persistent hypoxemia. With exceptionally low rate of false alarms, our proposed model is promising in improving clinical decision making and easing burden on the health system.
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Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO$_2$) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health, as well as improve physicians ability to remotely diagnose and treat respiratory conditions. In this work, we take a step towards this goal by performing the first clinical development validation on a smartphone-based SpO$_2$ sensing system using a varied fraction of inspired oxygen (FiO$_2$) protocol, creating a clinically relevant validation dataset for solely smartphone-based methods on a wide range of SpO$_2$ values (70%-100%) for the first time. This contrasts with previous studies, which evaluated performance on a far smaller range (85%-100%). We build a deep learning model using this data to demonstrate accurate reporting of SpO$_2$ level with an overall MAE=5.00% SpO$_2$ and identifying positive cases of low SpO$_2$<90% with 81% sensitivity and 79% specificity. We ground our analysis with a summary of recent literature in smartphone-based SpO2 monitoring, and we provide the data from the FiO$_2$ study in open-source format, so that others may build on this work.
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